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MissForest—non-parametric missing value imputation for mixed-type data
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MOTIVATION: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. RESULTS: We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. AVAILABILITY: The package missForest is freely available from http://stat.ethz.ch/CRAN/. CONTACT: stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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“Depending on the cause, the failure must be addressed in different ways, such as manual recalibration or data imputation [71,72].”
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Analysis of Incomplete Multivariate Data
Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627
Statistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
A multivariate technique for multiply imputing missing values using a sequence of regression models
Raghunathan, T. E., Lepkowski, J. M., Hoewyk, J. V., & Solenberger, P. W. (2001) · Survey methodology · Cited by 1,981
Flagged first, then by citations
Research works printed but not linked: 2
- Rubin, 1978, Multiple imputations in sample surveys-a phenomenological Bayesian approach to nonresponse, 20
- Rubin, 1990, Efficiently creating multiple imputations for incomplete multivariate normal data, 83
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Recurrent Neural Networks for Multivariate Time Series with Missing Values
Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24271-9 · Cited by 2,370
Real-time tracking of self-reported symptoms to predict potential COVID-19
Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., Ganesh, S., Varsavsky, T., Cardoso, M. J., El-Sayed Moustafa, J. S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0916-2 · Cited by 1,517
Hutter, F., Kotthoff, L., & Vanschoren, J. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5 · Cited by 1,691
missMDA : A Package for Handling Missing Values in Multivariate Data Analysis
Josse, J., & Husson, F. (2016) · Journal of Statistical Software · doi:10.18637/jss.v070.i01 · Cited by 1,423
Reddy, Y. N. V., Carter, R. E., Obokata, M., Redfield, M. M., & Borlaug, B. A. (2018) · Circulation · doi:10.1161/circulationaha.118.034646 · Cited by 1,294
Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage
Hendriksen, R. S., Munk, P., Njage, P., van Bunnik, B., McNally, L., Lukjancenko, O., Röder, T., Nieuwenhuijse, D., Pedersen, S. K., Kjeldgaard, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08853-3 · Cited by 1,199
Amazonia as a carbon source linked to deforestation and climate change
Gatti, L. V., Basso, L. S., Miller, J. B., Gloor, M., Gatti Domingues, L., Cassol, H. L. G., Tejada, G., Aragão, L. E. O. C., Nobre, C., Peters, W., et al. (2021) · Nature · doi:10.1038/s41586-021-03629-6 · Cited by 1,081
Random forests for genomic data analysis
Chen, X., & Ishwaran, H. (2012) · Genomics · doi:10.1016/j.ygeno.2012.04.003 · Cited by 988
Federated learning for predicting clinical outcomes in patients with COVID-19
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842
“A MissForest algorithm 71 was used to impute EMR features, based on the local training dataset.”
MethodShah, A. D., Bartlett, J. W., Carpenter, J. R., Nicholas, O., & Hemingway, H. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwt312 · Cited by 856
Random forest missing data algorithms
Tang, F., & Ishwaran, H. (2017) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11348 · Cited by 793
Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
Wei, R., Wang, J., Su, M., Jia, E., Chen, S., Chen, T., & Ni, Y. (2018) · Scientific Reports · doi:10.1038/s41598-017-19120-0 · Cited by 720
Understanding Random Forests: From Theory to PracticePreprint
Louppe, G. (2014) · arXiv (Cornell University) · doi:10.13140/2.1.1570.5928 · Cited by 1,101
Imputation with the R Package VIM
Kowarik, A., & Templ, M. (2016) · Journal of Statistical Software · doi:10.18637/jss.v074.i07 · Cited by 720
Dynamic landscape and regulation of RNA editing in mammals
Tan, M. H., Li, Q., Shanmugam, R., Piskol, R., Kohler, J. N., Young, A. N., Liu, K. I., Zhang, R., Ramaswami, G., Ariyoshi, K., et al. (2017) · Nature · doi:10.1038/nature24041 · Cited by 620
Missing value imputation: a review and analysis of the literature (2006–2017)
Lin, W.-C., & Tsai, C.-F. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09709-4 · Cited by 612
Alaa, A. M., Bolton, T. R., Angelantonio, E. D., Rudd, J. H. F., & van der Schaar, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213653 · Cited by 652
Rehbein, F., Kliem, S., Baier, D., Mößle, T., & Petry, N. M. (2015) · Addiction · doi:10.1111/add.12849 · Cited by 567
Sun, Z., Wang, G., Li, P., Wang, H., Min, Z., & Liang, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121549 · Cited by 633
Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass
Terrer, C., Jackson, R. B., Prentice, I. C., Keenan, T. F., Kaiser, C., Vicca, S., Fisher, J. B., Reich, P. B., Stocker, B. D., Hungate, B. A., et al. (2019) · Nature Climate Change · doi:10.1038/s41558-019-0545-2 · Cited by 565
Logistic regression was as good as machine learning for predicting major chronic diseases
Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., Wong, T. Y., & Cheng, C. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.002 · Cited by 597
Comparison of imputation methods for missing laboratory data in medicine
Waljee, A. K., Mukherjee, A., Singal, A. G., Zhang, Y., Warren, J. S., Balis, U. J., Marrero, J. A., Zhu, J., & Higgins, P. (2013) · BMJ Open · doi:10.1136/bmjopen-2013-002847 · Cited by 495
Global wildlife trade across the tree of life
Scheffers, B. R., Oliveira, B. F., Lamb, I., & Edwards, D. P. (2019) · Science · doi:10.1126/science.aav5327 · Cited by 478
Imputation of missing data in life‐history trait datasets: which approach performs the best?
Penone, C., Davidson, A. D., Shoemaker, K. T., Marco, M. D., Rondinini, C., Brooks, T. M., Young, B. E., Graham, C. H., & Costa, G. C. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12232 · Cited by 452
LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms
Money, D., Gardner, K. M., Migicovsky, Z., Schwaninger, H. R., Zhong, G., & Myles, S. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.021667 · Cited by 483
Metabolomic profiles predict individual multidisease outcomes
Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01980-3 · Cited by 406
Machine Learning Methods to Predict Diabetes Complications
Dagliati, A., Marini, S., Sacchi, L., Cogni, G., Teliti, M., Tibollo, V., De Cata, P., Chiovato, L., & Bellazzi, R. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817706375 · Cited by 407
Fine-root traits in the global spectrum of plant form and function
Carmona, C. P., Bueno, C. G., Toussaint, A., Träger, S., Díaz, S. M., Moora, M., Munson, A. D., Pärtel, M., Zobel, M., & Tamme, R. (2021) · Nature · doi:10.1038/s41586-021-03871-y · Cited by 414
Carranza, E. J. M., & Laborte, A. G. (2014) · Computers & Geosciences · doi:10.1016/j.cageo.2014.10.004 · Cited by 401
Dreyfus, J., Audureau, É., Bohbot, Y., Coisne, A., Badie, Y. L., Bouchery, M., Flagiello, M., Bazire, B., Eggenspieler, F., Viau, F., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab679 · Cited by 380
Xue, B., Li, D., Lu, C., King, C. R., Wildes, T. S., Avidan, M. S., Kannampallil, T., & Abraham, J. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.2240 · Cited by 373
Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R. T., Dickerson, R. R., Lin, J., Boersma, K. F., et al. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c03834 · Cited by 384
Wang, K., Tian, J., Zheng, C., Yang, H., Ren, J., Liu, Y., Han, Q., & Zhang, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104813 · Cited by 361
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Gao, Y., Cai, G., Fang, W., Li, H., Wang, S., Chen, L., Yu, Y., Liu, D., Xu, S., Cui, P., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18684-2 · Cited by 358
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks
Yoon, J., Zame, W. R., & van der Schaar, M. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2874712 · Cited by 341
Global priorities for conservation across multiple dimensions of mammalian diversity
Brum, F. T., Graham, C. H., Costa, G. C., Hedges, S. B., Penone, C., Radeloff, V. C., Rondinini, C., Loyola, R. A., & Davidson, A. D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706461114 · Cited by 338
Machine learning approach for pavement performance prediction
Marcelino, P., Antunes, M. D. L., Fortunato, E., & Gomes, M. C. (2019) · International Journal of Pavement Engineering · doi:10.1080/10298436.2019.1609673 · Cited by 342
Aromaticity and degree of aromatic condensation of char
Wiedemeier, D. B., Abiven, S., Hockaday, W. C., Keiluweit, M., Kleber, M., Masiello, C. A., McBeath, A. V., Nico, P., Pyle, L. A., Schneider, M. P. W., et al. (2014) · Organic Geochemistry · doi:10.1016/j.orggeochem.2014.10.002 · Cited by 330
Segar, M. W., Patel, K. V., Ayers, C. R., Basit, M. A., Tang, W. H. W., Willett, D. L., Berry, J. D., Grodin, J. L., & Pandey, A. (2019) · European Journal of Heart Failure · doi:10.1002/ejhf.1621 · Cited by 308
Complexity of avian evolution revealed by family-level genomes
Stiller, J., Feng, S., Chowdhury, A.-A., Rivas-González, I., Duchêne, D. A., Fang, Q., Deng, Y., Kozlov, A. M., Stamatakis, A., Claramunt, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07323-1 · Cited by 316
A faecal microbiota signature with high specificity for pancreatic cancer
Kartal, E., Schmidt, T. S. B., Molina-Montes, E., Rodríguez-Perales, S., Wirbel, J., Maistrenko, O. M., Akanni, W. A., Alashkar Alhamwe, B., Alves, R. J., Carrato, A., et al. (2022) · Gut · doi:10.1136/gutjnl-2021-324755 · Cited by 290
Segar, M. W., Vaduganathan, M., Patel, K. V., McGuire, D. K., Butler, J., Fonarow, G. C., Basit, M. A., Kannan, V., Grodin, J. L., Everett, B. M., et al. (2019) · Diabetes Care · doi:10.2337/dc19-0587 · Cited by 307
Myocardial Metabolomics of Human Heart Failure With Preserved Ejection Fraction
Hahn, V. S., Petucci, C. J., Kim, M., Bedi, K. C., Wang, H., Mishra, S., Koleini, N., Yoo, E., Margulies, K. B., Arany, Z. P., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.061846 · Cited by 305
Recursive partitioning for missing data imputation in the presence of interaction effects
Doove, L. L., van Buuren, S., & Dusseldorp, E. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.10.025 · Cited by 297
Rice, T. W., Ishwaran, H., Hofstetter, W. L., Kelsen, D. P., Apperson-Hansen, C., & Blackstone, E. H. (2016) · Diseases of the Esophagus · doi:10.1111/dote.12533 · Cited by 281
Health Behavior Changes During COVID-19 Pandemic and Subsequent “Stay-at-Home” Orders
Knell, G., Robertson, M. C., Dooley, E. E., Burford, K., & Mendez, K. S. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17176268 · Cited by 280
Sun, Y., Li, J., Xu, Y., Zhang, T., & Wang, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120201 · Cited by 305
Hong, S., & Lynn, H. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01080-1 · Cited by 268
Zweck, E., Thayer, K. L., Helgestad, O. K. L., Kanwar, M. K., Ayouty, M., Garan, A. R., Hernández-Montfort, J., Mahr, C., Wencker, D., Sinha, S. S., et al. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.020085 · Cited by 276
Wang, S., Celebi, M. E., Zhang, Y., Yu, X., Lu, S., Yao, X., Zhou, Q., Miguel, M.-G., Tian, Y., Górriz, J. M., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.001 · Cited by 252
Erosion of global functional diversity across the tree of life
Carmona, C. P., Tamme, R., Pärtel, M., de Bello, F., Brosse, S., Capdevila, P., González‐M., R., González‐Suárez, M., Salguero‐Gómez, R., Vásquez‐Valderrama, M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf2675 · Cited by 265
Saggi, M. K., & Jain, S. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.11.031 · Cited by 240
Proteomic signatures improve risk prediction for common and rare diseases
Carrasco-Zanini, J., Pietzner, M., Davitte, J., Surendran, P., Croteau‐Chonka, D. C., Robins, C., Torralbo, A., Tomlinson, C., Grünschläger, F., Fitzpatrick, N. K., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03142-z · Cited by 254
A Benchmark for Data Imputation Methods
Jäger, S., Allhorn, A., & Bießmann, F. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.693674 · Cited by 268
Divíšek, J., Chytrý, M., Beckage, B., Gotelli, N. J., Lososová, Z., Pyšek, P., Richardson, D. M., & Molofsky, J. (2018) · Nature Communications · doi:10.1038/s41467-018-06995-4 · Cited by 240
A methodology to derive global maps of leaf traits using remote sensing and climate data
Moreno‐Martínez, Á., Camps‐Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P. M., Krämer, K., Cornelissen, J. H. C., Reich, P. B., Bahn, M., et al. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.09.006 · Cited by 242
Deep-learning cardiac motion analysis for human survival prediction
Bello, G. A., Dawes, T. J. W., Duan, J., Biffi, C., de Marvao, A., Howard, L. S. G. E., Gibbs, J. S. R., Wilkins, M. R., Cook, S. A., Rueckert, D., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0019-2 · Cited by 237
Rodebaugh, T. L., Tonge, N., Piccirillo, M. L., Fried, E. I., Horenstein, A., Morrison, A. S., Goldin, P. R., Gross, J. J., Lim, M. H., Fernandez, K. C., et al. (2018) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000336 · Cited by 245
Wang, F., Huang, S., Gao, R., Zhou, Y., Lai, C., Li, Z., Xian, W., Qian, X., Li, Z., Huang, Y., et al. (2020) · Cell Discovery · doi:10.1038/s41421-020-00231-4 · Cited by 228
Machine Learning in Epidemiology and Health Outcomes Research
Wiemken, T. L., & Kelley, R. R. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094437 · Cited by 239
Funkhouser, C. J., Chacko, A., Correa, K. A., Kaiser, A. J. E., & Shankman, S. A. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13256 · Cited by 224
Epigenetic inheritance of diet-induced and sperm-borne mitochondrial RNAs
Tomar, A., Gomez-Velazquez, M., Gerlini, R., Comas-Armangué, G., Makharadze, L., Kolbe, T., Boersma, A., Dahlhoff, M., Burgstaller, J. P., Lassi, M., et al. (2024) · Nature · doi:10.1038/s41586-024-07472-3 · Cited by 216
Predicting Alzheimer's disease progression using deep recurrent neural networks
Nguyen, M., He, T., An, L., Alexander, D. C., Feng, J., Yeo, B. T. T., & Initiative, F. T. A. D. N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117203 · Cited by 228
Do, K. T., Wahl, S., Raffler, J., Molnos, S., Laimighofer, M., Adamski, J., Suhre, K., Strauch, K., Peters, A., Gieger, C., et al. (2018) · Metabolomics · doi:10.1007/s11306-018-1420-2 · Cited by 206
Plant sizes and shapes above and belowground and their interactions with climate
Tumber‐Dávila, S. J., Schenk, H. J., Du, E., & Jackson, R. B. (2022) · New Phytologist · doi:10.1111/nph.18031 · Cited by 204
Boursier, J., Hagström, H., Ekstedt, M., Moreau, C., Bonacci, M., Cure, S., Ampuero, J., Nasr, P., Tallab, L., Canivet, C. M., et al. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2021.12.031 · Cited by 206
Nikookar, E., & Yanadori, Y. (2021) · International Journal of Operations & Production Management · doi:10.1108/ijopm-04-2021-0272 · Cited by 229
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 217
Carranza, E. J. M., & Laborte, A. G. (2015) · Natural Resources Research · doi:10.1007/s11053-015-9268-x · Cited by 195
Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
Gromski, P. S., Yun Xu, A., Kotze, H. L., Correa, E. S., Ellis, D. I., Armitage, E. G., Turner, M. L., & Goodacre, R. (2014) · Metabolites · doi:10.3390/metabo4020433 · Cited by 197
Karhade, A., Thio, Q., Ogink, P., Bono, C. M., Ferrone, M., Oh, K., Saylor, P., Schoenfeld, A. J., Shin, J. H., Harris, M. B., et al. (2019) · Neurosurgery · doi:10.1093/neuros/nyz070 · Cited by 179
Roadkill risk and population vulnerability in European birds and mammals
Grilo, C., Koroleva, E. G., Andrášik, R., Bíl, M., & González‐Suárez, M. (2020) · Frontiers in Ecology and the Environment · doi:10.1002/fee.2216 · Cited by 188
Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B. J., Ngo, T. T. M., Lee‐McMullen, B., Lehallier, B., Robaczewska, A., McIlwain, D. R., Rosenberg‐Hasson, Y., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty537 · Cited by 183
Predicting Missing Values in Medical Data Via XGBoost Regression
Zhang, X., Yan, C., Gao, C., Malin, B., & Chen, Y. (2020) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00077-1 · Cited by 185
“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling
Klåvus, A., Kokla, M., Noerman, S., Koistinen, V., Tuomainen, M. H., Zarei, I., Meuronen, T., Häkkinen, M. R., Rummukainen, S., Babu, A. F., et al. (2020) · Metabolites · doi:10.3390/metabo10040135 · Cited by 187
Gago, J., Daloso, D. M., Figueroa, C. M., Flexas, J., Fernie, A. R., & Nikoloski, Z. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.15.01660 · Cited by 188
Missing data imputation with adversarially-trained graph convolutional networks
Spinelli, I., Scardapane, S., & Uncini, A. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.06.005 · Cited by 196
Machine Learning in Nutrition Research
Kirk, D., Kok, E. J., Tufano, M., Teki̇nerdoğan, B., Feskens, E. J. M., & Camps, G. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac103 · Cited by 206
Predicting future invaders and future invasions
Fournier, A., Penone, C., Pennino, M. G., & Courchamp, F. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803456116 · Cited by 183
Wei, R., Wang, J., Jia, E., Chen, T., Ni, Y., & Jia, W. P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005973 · Cited by 175
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Hawk, S. T., van den Eijnden, R. J. J. M., van Lissa, C. J., & ter Bogt, T. F. M. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.10.032 · Cited by 157
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Metadata
- Authors
- Stekhoven, D. J., & Bühlmann, P.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Stekhoven, Daniel J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-10-28
- Volume
- 28
- Issue
- 1
- Pages
- 112-118
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2064186732
- Cited by
- 6,156
- References
- 27
- Citations by year
- 2026: 901 · 2025: 1098 · 2024: 933 · 2023: 794 · 2022: 634 · 2021: 614 · 2020: 397 · 2019: 250 · 2018: 212 · 2017: 114 · 2016: 86 · 2015: 51 · 2014: 39 · 2013: 18 · 2012: 10
- FWCI
- 19.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- missing data imputation, mixed data, missForest, random forest, multiple imputation, high-throughput data, high-dimensional data, biological data
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Escherichia coli, Humans, Arabidopsis, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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